SENIOR MACHINE LEARNING ENGINEER

Huntington Ingalls•Newport News, VA

9h•Hybrid

About The Position

Design, develop, and test operating systems-level software, compilers, and network distribution software. Set operational specifications, and formulate and analyze software requirements. May design embedded systems software. We are seeking a Senior Machine Learning Engineer to join our dynamic Data Science team. In this role, you will design, build, and optimize machine learning solutions that power critical business applications. You will work closely with data scientists and engineers to implement scalable ML pipelines using ML Ops best practices, ensuring models are efficiently deployed, monitored, and maintained in production environments. This position requires a strong technical foundation, a collaborative mindset, and a passion for delivering high-quality, data-driven solutions.Key responsibilities include:• Developing and maintaining ML pipelines for training, deployment, and monitoring.• Collaborating with data scientists to operationalize models and improve performance.• Implementing CI/CD workflows for ML systems.• Ensuring reproducibility, scalability, and reliability of ML solutions.• Driving automation and optimization across the ML lifecycle.

Requirements

Bachelor's Degree and 9 years of relevant exempt experience; Master's Degree and 7 years of relevant professional experience; Ph.D. and 4 years of experience.
Bachelor’s degree in Computer Science, Data Science, Engineering, or related field.
9 years of experience in software engineering with a focus on backend or systems development. Master's degree and 7 years of relevant experience. PhD and 4 years of experience.
Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch).
Hands-on experience with ML Ops tools and practices (e.g., MLflow, Kubeflow, Airflow).
Strong understanding of cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes).
Experience with CI/CD pipelines and version control (Git).
Excellent problem-solving and communication skills.

Nice To Haves

Master’s degree in Computer Science, Machine Learning, or related discipline.
Experience with large-scale data processing (Spark, Hadoop).
Knowledge of feature stores and model monitoring tools.
Familiarity with distributed training and optimization techniques.
Background in building ML solutions for real-time or high-throughput systems.
Contributions to open-source ML or DevOps projects.
Strong understanding of security and compliance in ML deployments.

Responsibilities

Developing and maintaining ML pipelines for training, deployment, and monitoring.
Collaborating with data scientists to operationalize models and improve performance.
Implementing CI/CD workflows for ML systems.
Ensuring reproducibility, scalability, and reliability of ML solutions.
Driving automation and optimization across the ML lifecycle.